Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.587743
Title: Dynamic models of continuous and discrete outcomes : methods and applications
Author: Ypma, J. Y.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Abstract:
This thesis contains three chapters on dynamic models with discrete and continuous outcomes. In the rest chapter, I focus on indirect inference estimation. Indirect inference is used to estimate parameters in models where evaluation of the objective function directly is complicated or infeasible. Indirect inference is typically formulated as an optimization problem nesting one or more other optimization problems. In some cases the solution to the inner optimization problems can be obtained in one step, but when such a solution is not available, indirect inference estimation is computationally demanding. I show how constrained optimization methods can be used to replace the nesting of optimization problems and I provide Monte Carlo evidence showing when this approach is bene cial. The second chapter uses panel data from the United Kingdom to estimate a model of wage dynamics with labour participation where the variance in wages is decomposed in a permanent and a transitory component. Most studies that estimate similar models ignore non-participation; individuals without a wage are simply removed from the analysis. This leads to biased estimates of the parameters if working individuals are di erent in their unobservable characteristics compared to people that do not work. I use a dynamic selection model to include a discrete labour participation choice in a simple model of wage dynamics and compare the results to a version of the model that does not include labour participation. In the third chapter, I show how some of the assumptions on the dynamics of the unobservables in the second chapter can be relaxed. High dimensional integrals have to be approximated to estimate the less restrictive models. I use sparse grids and simulation methods to approximate these integrals and compare their performance on simulated data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.587743  DOI: Not available
Share: